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5th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2021

作者： Ye, Yuguang Quanzhou Normal University fujian Quanzhou362000 China Fujian Provincial Key Laboratory of Data Intensive Computing fujian Quanzhou362000 China Key Laboratory of Intelligent Computing and Information Processing Fujian Province University fujian Quanzhou362000 China

ISBN: (纸本)9781665435246

With the rapid development of the information technology era, the era of big data has also arrived. While computer networks are promoting the prosperity and development of society, their applications have become more extensive and in-depth. Smart city video surveillance systems have entered an era of networked surveillance and business integration. The problems are also endless. This article discusses computer network security in the era of big data, hoping to help strengthen the security of computer networks in our country. This paper studies the computer network security prevention strategies of smart cities in the era of big data. © 2021 IEEE.

关键词： Big data

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Energy-efficient VM placement algorithms for cloud data center 2nd

Energy-efficient VM placement algorithms for cloud data cent...

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2nd International Conference on Cloud computing and Big Data, CloudCom-Asia 2015

作者： Lin, Xiuyan Liu, Zhanghui Guo, Wenzhong Fujian Provincial Key Lab of the Network Computing and Intelligent Information Processing College of Mathematics and Computer Science Fuzhou University Fuzhou350116 China

ISBN: (纸本)9783319284293

Cloud is the computing paradigm which provides computing resource as a service through network. The client can use computing resource in a convenient and on-demand way, just like the water and the electricity we use daily. The mapping between virtual machine and physical machine is the key of the VM scheduling problem. Nowadays we advocate low-carbon life. It calls for the green cloud computing solutions whether protecting the environment or saving the cost of cloud suppliers. The proposed VM placement algorithm is energy-efficient, and considers the multi-dimentional resource constrains, such as CPU, memory, network bandwidth, and so on. The experimental results show that the proposed algorithms not only contribute a lot to energy saving, but also try best to meet the quality of service (QoS). Therefore, we make significant savings in operating cost and make full use of various resources in the cloud data center. The algorithm has promising prospect in application. © Springer International Publishing Switzerland 2015.

关键词： Virtual machine

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Regression-based execution time prediction in hadoop environment

Regression-based execution time prediction in hadoop environ...

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International Conference on information Technology and Computer Application Engineering, ITCAE 2014

作者： Wu, Xiao Dong Zeng, Yu Zhu Zhao, C.T. Faculty of Mathematics and Computer Science Quanzhou Normal University China Fujian Provincial Key Laboratory of Data Intensive Computing China Key Laboratory of Intelligent Computing and Information Processing Fujian Province University China

ISBN: (纸本)9781138027176

Hadoop MapReduce has been proven an effective computing model to deal with big data for the last few years. However, one technical challenge facing this framework is how to predict the execution time of an individual job. In this paper, we propose a regression-based method to predict the execution times of Hadoop jobs. The proposed prediction method consists of three phases: learning data sampling, regression analysis and making prediction. Experimental results show that the predicted execution time obtained by our method is close to the actual execution time. © 2015 Taylor & Francis Group, London.

关键词： Regression analysis

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No-Reference Image Quality Assessment Based on Multi-scale Convolutional Neural networks

No-Reference Image Quality Assessment Based on Multi-scale C...

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computing Conference, 2019

作者： Chen, Peikun Niu, Yuzhen Huang, Dong College of Mathematics and Computer Science Fuzhou University Fuzhou350116 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China

ISBN: (纸本)9783030228675

Big data brings more challenges to image quality assessment (IQA). First of all, reference images are not always available, so full-reference IQA (FR-IQA) and reduced-reference IQA (RR-IQA) can be problematic for big data. Because of no requirement for any pristine knowledge, no-reference IQA (NR-IQA) has recently received a great deal of attention. In recent years, deep convolution neural network (CNN) has achieved many successes in visual recognition tasks. Researches in literature have shown that NR-IQA metrics can be learned from features extracted using deep CNN. But existing CNN-based IQA models focus on local spatial information but ignore global spatial structure. In this paper, we propose a NR-IQA model learns from multi-scale CNN (MSCNN). Multi-scale mechanism has boosted the performances of many singe-scale methods in a variety of applications. MSCNN is a multi-scale CNN model which uses fully connected layers on top of multiple single-scale CNN models which responsible for feature extraction at different scales. In this way, the features extracted at multiple scales work together to assess image quality. Extensive experiments are conducted on the LIVE database and the experimental results validate the effectiveness of our MSCNN index compared to typical FR-IQA and state-of-the-art NR-IQA metrics. Furthermore, the results of cross database experiments show that MSCNN has good generalization ability. © 2019, Springer Nature Switzerland AG.

关键词： Big data

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A Deep Reinforcement Learning Framework for Vehicle Detection and Pose Estimation in 3D Point Clouds 6th

A Deep Reinforcement Learning Framework for Vehicle Detectio...

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6th International Conference on Artificial Intelligence and Security, ICAIS 2020

作者： Wang, Weipeng Luo, Huan Zheng, Quan Wang, Cheng Guo, Wenzhong Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350003 China Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou350003 China Fujian Key Laboratory of Sensing and Computing for Smart City Xiamen University XiamenFJ361005 China

ISBN: (纸本)9783030578800

As for autonomous driving in urban environments, it is of significance to accurately capture the position and pose of vehicles. Those information can assist self-driving system in making right decisions to avoid potential risks. Currently, 3D point clouds captured by laser scanners are widely used in self-driving systems to sense the real environment. Therefore, in this paper, we propose a deep reinforcement learning framework for vehicle detection and pose estimation by using 3D point clouds. Specifically, to estimate the pose of vehicles, we propose to design a rotation action in Deep Q network (DQN). In addition, by considering the whole detection procedure as Markov Decision Process (MDP), our intermediate detected results can further improve the detection performance of our proposed method. The evaluations are carried on outdoor point cloud scenes captured by VMX450 laser scanning system. The experimental results demonstrate the satisfied performance on vehicle detection and pose estimation. © 2020, Springer Nature Switzerland AG.

关键词： Reinforcement learning

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Data secure transmission intelligent prediction algorithm for mobile industrial IoT networks

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Digital Communications and networks 2023年第2期9卷 400-410页

作者： Lingwei Xu Hao Yin Hong Jia Wenzhong Lin Xinpeng Zhou Yong Fu Xu Yu Department of Information Science and Technology Qingdao University of Science and TechnologyQingdao266061China Fujian Key Laboratory of Sensing and Computing for Smart Cities Xiamen UniversityXiamen361005China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control(Minjiang University) Fuzhou350121China Faculty of Information Science and Engineering Ocean University of ChinaQingdao266100China Shandong Provincial Key Laboratory of Computer Networks Shandong Computer Science Center(National Supercomputer Center in Jinan)JinanChina

Mobile Industrial Internet of Things(IIoT)applications have achieved the explosive growth in recent *** mobile IIoT has flourished and become the backbone of the industry,laying a solid foundation for the interconnection of all *** variety of application scenarios has brought serious challenges to mobile IIoT networks,which face complex and changeable communication *** data secure transmission is critical for mobile IIoT *** paper investigates the data secure transmission performance prediction of mobile IIoT *** cut down computational complexity,we propose a data secure transmission scheme employing Transmit Antenna Selection(TAS).The novel secrecy performance expressions are first ***,to realize real-time secrecy analysis,we design an improved Convolutional Neural network(CNN)model,and propose an intelligent data secure transmission performance prediction *** mobile signals,the important features may be removed by the pooling *** will lead to negative effects on the secrecy performance prediction.A novel nine-layer improved CNN model is *** of the input and output layers,it removes the pooling layer and contains six convolution ***,Back-Propagation(BP)and LeNet methods are employed to compare with the proposed *** simulation analysis,good prediction accuracy is achieved by the CNN *** prediction accuracy obtains a 59%increase.

关键词： Mobile IIoT networks Data secure transmission Performance analysis intelligent prediction Improved CNN

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The analysis of big Data application mode and security risk

The analysis of big Data application mode and security risk

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International Conference on information Sciences,Machinery,Materials and Energy（ICISMME 2015）

作者： ZENG Tai-sheng Faculty of Mathematics and Computer Science Quanzhou Normal University Fujian Provincial Key Laboratory of Data Intensive Computing Key Laboratory of Intelligent Computing and Information Processing Fujian Province University

ISBN: (纸本)9789462520677

In the current social background,Existing facilities and tools already can not meet the needs of big data in expanding and analysis ***'s data storage and analysis work is achieved under cloud conditions and Hadoop were set *** the conditions for cloud computing,cloud computing applications who have remote data files were not authorized to read its contents,which results in unauthorized manipulation,and it will produce a lot of security risks for large *** this paper,according to the cloud of different modes,Hadoop different stages of the operation,subject to the threat of non- confidence and security to steal big data generated to analyze a variety of privacy,with threat model as an example,it explores ways to address security threats.

关键词： big data security analysis Hadoop

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Conditional selection with CNN augmented transformer for multimodal affective analysis

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 917-931页

作者： Jianwen Wang Shiping Wang Shunxin Xiao Renjie Lin Mianxiong Dong Wenzhong Guo College of Computer and Data Science Fuzhou UniversityFuzhouChina College of Computer and Cyber Security Fujian Normal UniversityFuzhouChina Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou UniversityFuzhouChina Digital Fujian Institute of Big Data Security Technology Fujian Normal UniversityFuzhouChina Department of Sciences and Informatics Muroran Institute of TechnologyMuroranJapan

Attention mechanism has been a successful method for multimodal affective analysis in recent years. Despite the advances, several significant challenges remain in fusing language and its nonverbal context information. One is to generate sparse attention coefficients associated with acoustic and visual modalities, which helps locate critical emotional se-mantics. The other is fusing complementary cross‐modal representation to construct optimal salient feature combinations of multiple modalities. A Conditional Transformer Fusion network is proposed to handle these problems. Firstly, the authors equip the transformer module with CNN layers to enhance the detection of subtle signal patterns in nonverbal sequences. Secondly, sentiment words are utilised as context conditions to guide the computation of cross‐modal attention. As a result, the located nonverbal fea-tures are not only salient but also complementary to sentiment words directly. Experi-mental results show that the authors’ method achieves state‐of‐the‐art performance on several multimodal affective analysis datasets.

关键词： affective computing data fusion information fusion multimodal approaches

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A Local Regression-Based SLA-Aware Energy-Efficient Virtual Machine Consolidation Algorithm in Data Centers 9

A Local Regression-Based SLA-Aware Energy-Efficient Virtual ...

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9th International Conference on Big Data computing and Communications, BigCom 2023

作者： Wu, Xiaodong Wang, Ronghai Lin, Guoxin Quanzhou Normal University School of Mathmatics and Computer Science China Fujian Province University Fujian Provincial Key Laboratory of Data-Intensive Computing Key Laboratory of Intelligent Computing and Information Processing Fujian China Provincial Big Data Research Institute of Intelligent Manufacturing Quanzhou China

ISBN: (纸本)9798350331240

Virtual Machine Consolidation (VMC) is an effective means to improve the resource utilization and reduce energy consumption of data centers. However, the VMC optimization that only considers energy consumption may lead to SLA (Service-Level Agreements) violations and thus fail to guarantee the QoS (Quality of Service) provided. Therefore, it is necessary to consider both energy saving and SLA violations in the process of VMC. In this paper, we propose an energy and SLA-aware VMC algorithm that considers both energy saving and SLA violation optimization in data centers. The proposed algorithm uses the local regression method to predict the utilization of virtual machines, based on which it determines whether the host will be overloaded, so as to make the decision of virtual machine migrations. The proposed VMC algorithm is evaluated with CloudSim toolkit. Simulation results demonstrate that our algorithm has good energy saving performance and can effectively reduce SLA violation in cloud data center. Considering energy consumption and SLA violation, the ESV can be reduced by 7.57% on average and 16.36% at most compared with other algorithms. © 2023 IEEE.

关键词： Virtual machine

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Lightweight spatial pyramid pooling network for real-time semantic segmentation

Lightweight spatial pyramid pooling network for real-time se...

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2021 International Symposium on intelligent Robotics and Systems, ISoIRS 2021

作者： Jiang, Peilong Ke, Xiao Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing College of Computer and Data Science Fuzhou University Fuzhou350116 China

In recent years, the state-of-the-art semantic segmentation models have made extremely successful in various challenging scenes. However, the high computation costs of these models make it difficult to deploy to mobile devices. To better serve in computation constraint scenes, the semantic segmentation model should not only have high segmentation performance, but also fast inference speed. In this paper, we proposed an efficient multi-scale context module named LSPPM, which can gather abundant context information at a low computation cost. Base on this, we present a real-time semantic segmentation model called LSPPNet which is specially designed for real-time application. We have done an exhaustive experiment to evaluate LSPPNet in the challenge urban street scenes datasets Cityscapes. Extensive experiment shows that LSPPNet gets a better trade-off between segmentation performance and inference speed. We test LSPPNet on an NVIDIA 2080 super graphics card and it can achieve 75.8% MIoU in Cityscapes test set in real-time speed. © Published under licence by IOP Publishing Ltd.

关键词： Semantic Segmentation

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